{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.animation as animation\n",
    "from IPython.display import HTML\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_media(iflaga):\n",
    "    global nx, ny, mxst, mxnd, myst, mynd, mediaEz, mediaHx, mediaHy\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    if (iflaga == 2):\n",
    "        for  i in range(nx):\n",
    "            for j in range(ny):\n",
    "                if (i >= mxst-1) and (i <= mxnd-1):\n",
    "                    if ( j >= myst-1) and (j <= mynd-1):\n",
    "                        mediaEz[i,j] = 2\n",
    "        \n",
    "        for  i in range(nx):\n",
    "            for j in range(ny):\n",
    "                if (i >= mxst-1) and (i <= mxnd-1):\n",
    "                    if ( j >= myst-1) and (j <= mynd-2):\n",
    "                        mediaHx[i,j] = 2\n",
    "\n",
    "        for  i in range(nx):\n",
    "            for j in range(ny):\n",
    "                if (i >= mxst-1) and (i <= mxnd-2):\n",
    "                    if ( j >= myst-1) and (j <= mynd-1):\n",
    "                        mediaHy[i,j] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_coefficients():\n",
    "\n",
    "    global Ca, Cb, Da, Db  # Define material based coefficients\n",
    "    global xmu, eps0, dt, ds\n",
    "    Ca = np.zeros((2, 1))\n",
    "    Cb = np.zeros((2, 1))\n",
    "    Da = np.zeros((2, 1))\n",
    "    Db = np.zeros((2, 1))\n",
    "    # Field Coefficients\n",
    "    dte = dt/(ds*eps0)\n",
    "    dtm = dt/(ds*xmu)\n",
    "    Da[0] = 1\n",
    "    Db[0] = dtm\n",
    "    Ca[0] = 1\n",
    "    Cb[0] = dte\n",
    "    Da[1] = 0\n",
    "    Db[1] = 0\n",
    "    Ca[1] = 0\n",
    "    Cb[1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Source(n, sources):\n",
    "    global Ezs\n",
    "    # Creates a half-sinusoidal source between the time increments\n",
    "    # 1 and 10.%\n",
    "    # When source = 1 : Sinusoid\n",
    "    #               2 : Gaussian\n",
    "    #\n",
    "    ## For Gaussian Source\n",
    "    if sources == 2:\n",
    "            xndec = 10.0\n",
    "            xn0 = 4*xndec\n",
    "            Ezs = math.exp(-((n-xn0)/(xndec))**2)\n",
    "        ## For Sinusoidal Source\n",
    "    elif sources == 1:\n",
    "        if ( n >=1-1) and (n <= 10-1):\n",
    "            Ezs = math.sin(n*math.pi/10)\n",
    "    return Ezs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adv_Ez(n, sources):\n",
    "    global Ez, Hx, Hy\n",
    "    global mediaEz\n",
    "    global Ca, Cb\n",
    "    global nx, ny\n",
    "    for i in range(nx):\n",
    "        for j in range(ny):\n",
    "            m  = int(mediaEz[i, j]-1)\n",
    "            if (i == 0):\n",
    "                Ez[i, j] = Source(n, sources)\n",
    "            elif (i >= 1 and j >=1):\n",
    "                Ez[i, j] = Ez[i, j]*Ca[m] + Cb[m]*(Hy[i, j] - Hy[i-1, j] - (Hx[i, j] - Hx[i, j-1]))\n",
    "            elif (j == 0):\n",
    "                Ez[i, j] = Ez[i, j]*Ca[m] + Cb[m]*(Hy[i, j] - Hy[i-1, j]- Hx[i, j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adv_H(n):\n",
    "    global Ez, Hx, Hy\n",
    "    global mediaHx, mediaHy\n",
    "    global Da, Db\n",
    "    global nx, ny\n",
    "    for i in range(nx):\n",
    "        for j in range(ny-1):\n",
    "            m = int(mediaHx[i, j]-1)\n",
    "            Hx[i, j] = Hx[i, j]*Da[m] - Db[m]*(Ez[i, j+1] - Ez[i, j])\n",
    "    for i in range(nx-1):\n",
    "        for j in range(ny):\n",
    "            m = int(mediaHy[i, j]-1)\n",
    "            Hy[i, j] = Hy[i, j]*Da[m] + Db[m]*(Ez[i+1, j] - Ez[i, j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_surface_plot(field):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    xd = np.linspace(0, 10, 100)\n",
    "    yd = np.linspace(0, 10, 80)\n",
    "    [xdg, ydg] = np.meshgrid(xd, yd)\n",
    "    dem3d = ax.plot_surface(xdg, ydg, field, cmap='rainbow', edgecolor='none')\n",
    "    fig.colorbar(dem3d, shrink=0.5, aspect=5)\n",
    "\n",
    "\n",
    "def my_line_plot(Ez_5, Ez_35, Ez_65, Ez_95,iflaga, source):\n",
    "    maxval=1.4\n",
    "    fig = plt.figure(figsize=(6,7))\n",
    "    #plt.subplots_adjust(wspace =0, hspace =1)\n",
    "    plt.subplot(411)\n",
    "    plt.grid()\n",
    "    ax1 = plt.gca()\n",
    "    ax1.set_ylim([-maxval,maxval])\n",
    "    plt.plot(range(len(Ez_5)),Ez_5,'k')\n",
    "    #plt.xticks([])\n",
    "    plt.legend(title='n=5', loc='best', frameon=False)\n",
    "    \n",
    "\n",
    "    plt.subplot(412)\n",
    "    plt.grid()\n",
    "    ax2 = plt.gca()\n",
    "    ax2.set_ylim([-maxval,maxval])\n",
    "    plt.plot(range(len(Ez_35)),Ez_35, 'k')\n",
    "    #plt.xticks([])\n",
    "    plt.legend(title='n=35',loc='best', frameon=False)\n",
    "\n",
    "    plt.subplot(413)\n",
    "    plt.grid()\n",
    "    ax3 = plt.gca()\n",
    "    ax3.set_ylim([-maxval,maxval])\n",
    "    plt.plot(range(len(Ez_65)),Ez_65,'k')\n",
    "    #plt.xticks([])\n",
    "    plt.legend(title='n=65',loc='best', frameon=False)\n",
    "\n",
    "    plt.subplot(414)\n",
    "    plt.grid()\n",
    "    ax4 = plt.gca()\n",
    "    ax4.set_ylim([-maxval,maxval])\n",
    "    plt.plot(range(len(Ez_95)),Ez_95,'k')\n",
    "    #plt.xticks([])\n",
    "    plt.legend(title='n=95',loc='best', frameon=False)\n",
    "    if iflaga == 1 and source == 1:\n",
    "        fig.suptitle('Line Plots for E-field with no obstacle for a sinusoid source at j='+str(strip))\n",
    "    elif iflaga == 2 and source == 1:\n",
    "        fig.suptitle('Line Plots for E-field with a PEC box for a sinusoid source at j='+str(strip))\n",
    "    elif source == 2:\n",
    "        fig.suptitle('Line Plots for E-field with no obstacle for a Gaussian source at j='+str(strip))\n",
    "    # plt.title('Line Plots for E-field with no obstacle for a sinusoid source')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Routine to zero out the global variables\n",
    "#*************************\n",
    "# *************************\n",
    "def zeroing():\n",
    "# Clears but retains the variables in memory\n",
    "\n",
    "    global nx,  ny\n",
    "    global Ez,  Hx,  Hy # Create E and H field components.\n",
    "    # global data type enables the global scope of the variables\n",
    "    # within the code. Unlike global variables they can not be accessed\n",
    "    #outside the code\n",
    "    global mediaEz, mediaHx, mediaHy\n",
    "    global Ca, Cb, Da, Db # Define material based coefficients\n",
    "\n",
    "\n",
    "    Ez = np.zeros((nx,ny)) # z-component of E-field\n",
    "    Hx = np.zeros((nx,ny)) # x-component of H-field\n",
    "    Hy = np.zeros((nx,ny)) # y-component of H-field\n",
    "\n",
    "    mediaEz = np.ones((nx,ny)) # z-component of E-field\n",
    "    mediaHx = np.ones((nx,ny)) #x-component of H-field\n",
    "    mediaHy = np.ones((nx,ny)) # x-component of H-field\n",
    "\n",
    "    Ca = np.zeros((2,1)) # x-component of H-field\n",
    "    Cb = np.zeros((2,1)) # x-component of H-field\n",
    "    Da = np.zeros((2,1)) # x-component of H-field\n",
    "    Db = np.zeros((2,1)) # x-component of H-field\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_ex3():\n",
    "    ############## Parameters\n",
    "    global c, xmu, eps0, asize\n",
    "    global nx, ny, nt, mxst, mxnd, myst, mynd\n",
    "    global dt, ds\n",
    "    global Ez, Hx, Hy #Create E and H field components.\n",
    "    # global data type enables the global scope of the variables\n",
    "    # within the code.Unlike global variables they can not be accessed\n",
    "    # outside the code\n",
    "    global mediaEz, mediaHx, mediaHy\n",
    "    global Ca, Cb, Da, Db # Define material based coefficients\n",
    "    global Ezs # The excitation signal\n",
    "    global strip\n",
    "\n",
    "    c = 2.99792458e8\n",
    "    xmu = 4 * math.pi * 10**(-7)\n",
    "    eps0 = 8.854187817*10**(-12)\n",
    "    asize = 5\n",
    "    nx = 80\n",
    "    ny = 100\n",
    "    nt = 100\n",
    "    mxst = 17\n",
    "    mxnd = 49\n",
    "    myst = 33\n",
    "    mynd = 65\n",
    "    strip = 65\n",
    "\n",
    "    Ez = np.zeros((nx, ny),dtype='longdouble')\n",
    "    Hx = np.zeros((nx, ny),dtype='longdouble')\n",
    "    Hy = np.zeros((nx, ny),dtype='longdouble')\n",
    "\n",
    "    mediaEz = np.ones((nx, ny),dtype='longdouble')\n",
    "    mediaHx = np.ones((nx, ny),dtype='longdouble')\n",
    "    mediaHy = np.ones((nx, ny),dtype='longdouble')\n",
    "\n",
    "    Ca = np.zeros((2, 1),dtype='longdouble')\n",
    "    Cb = np.zeros((2, 1),dtype='longdouble')\n",
    "    Da = np.zeros((2, 1),dtype='longdouble')\n",
    "    Db = np.zeros((2, 1),dtype='longdouble')\n",
    "\n",
    "\n",
    "    ds = asize / (mxnd - mxst - 1)\n",
    "    dt = ds / (c * math.sqrt(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_ex3()\n",
    "iflaga = 1\n",
    "define_media(iflaga)\n",
    "define_coefficients()\n",
    "source = 1\n",
    "for x in range(nt):\n",
    "    n = x+1\n",
    "    adv_Ez(n, source)\n",
    "    adv_H(n)\n",
    "    #if n % 5 == 0:\n",
    "        #my_surface_plot(Ez)\n",
    "        #plt.title('3D plot of $E_z$ in free-space')\n",
    "        #plt.show()\n",
    "\n",
    "    if n == 5:\n",
    "        Ez_5 = copy.deepcopy(Ez[:, strip-1])\n",
    "    elif n == 35:\n",
    "        Ez_35 = copy.deepcopy(Ez[:, strip-1])\n",
    "    elif n == 65:\n",
    "        Ez_65 = copy.deepcopy(Ez[:, strip-1])\n",
    "    elif n == 95:\n",
    "        Ez_95 = copy.deepcopy(Ez[:, strip-1])\n",
    "\n",
    "my_line_plot(Ez_5, Ez_35, Ez_65, Ez_95, iflaga, source)\n",
    "zeroing()\n",
    "\n",
    "source = 2\n",
    "define_media(iflaga)\n",
    "define_coefficients()\n",
    "\n",
    "for n in range(nt):\n",
    "    adv_Ez(n, source)\n",
    "    adv_H(n)\n",
    "    if n == 5:\n",
    "        Ez_5 = copy.deepcopy(Ez[:, strip-1])\n",
    "    elif n == 35:\n",
    "        Ez_35 = copy.deepcopy(Ez[:, strip-1])\n",
    "    elif n == 65:\n",
    "        Ez_65 = copy.deepcopy(Ez[:, strip-1])\n",
    "    elif n == 95:\n",
    "        Ez_95 = copy.deepcopy(Ez[:, strip-1])\n",
    "\n",
    "my_line_plot(Ez_5, Ez_35, Ez_65, Ez_95, iflaga, source)\n",
    "zeroing()\n",
    "\n",
    "iflaga = 2\n",
    "define_media(iflaga)\n",
    "define_coefficients()\n",
    "source = 1\n",
    "\n",
    "for n in range(nt):\n",
    "    adv_Ez(n, source)\n",
    "    adv_H(n)\n",
    "    #if n % 5 == 0:\n",
    "        #my_surface_plot(Ez)\n",
    "        #plt.title('3D plot of $E_z$ with PEC box')\n",
    "        #plt.show()\n",
    "\n",
    "    if n == 5-1:\n",
    "        Ez_5 = copy.deepcopy(Ez[:, strip-1])\n",
    "    elif n == 35-1:\n",
    "        Ez_35 = copy.deepcopy(Ez[:, strip-1])\n",
    "    elif n == 65-1:\n",
    "        Ez_65 = copy.deepcopy(Ez[:, strip-1])\n",
    "    elif n == 95-1:\n",
    "        Ez_95 = copy.deepcopy(Ez[:, strip-1])\n",
    "\n",
    "my_line_plot(Ez_5, Ez_35, Ez_65, Ez_95, iflaga, source)\n",
    "zeroing()\n",
    "define_media(iflaga)\n",
    "define_coefficients()\n",
    "\n",
    "for n in range(nt):\n",
    "    adv_Ez(n, source)\n",
    "    adv_H(n)\n",
    "    #if n % 5 == 0:\n",
    "        #my_surface_plot(Hx)\n",
    "        #plt.title('3D plot of $H_x$ with PEC box')\n",
    "        #plt.show()\n",
    "\n",
    "zeroing()\n",
    "define_media(iflaga)\n",
    "define_coefficients()\n",
    "\n",
    "for n in range(nt):\n",
    "    adv_Ez(n, source)\n",
    "    adv_H(n)\n",
    "    #if n % 5 == 0:\n",
    "        #my_surface_plot(Hy)\n",
    "        #plt.title('3D plot of $H_y$ with PEC box')\n",
    "        #plt.show()"
   ]
  }
 ],
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